发表机构
Indian Institute of Technology Ropar; Kroop AI; Flinders University（印度罗帕尔理工学院; 克鲁普人工智能公司; 弗林德斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DREAMS数据集，将用户参与度与注意力状态分析视为分类问题，对比单任务、迁移学习、多任务设置的性能，发现迁移与多任务学习的参与度分类表现更优，且二者与认知负荷、任务表现相关，数据集与代码公开。
AI 中文摘要
主动注意力和参与度对提升用户学习体验至关重要。参与度指个体对特定任务表现出的投入程度与兴趣水平；注意力则指个体有意识地完全聚焦于特定任务的状态。二者是不同但紧密关联的概念，可相互双向影响。为探究用户参与度与注意力的关系，本文提出了Diverse Reactions of Engagement and Attention Mind States（DREAMS）数据集。该数据集包含32名用户在自然场景下观看各类刺激以唤起不同情绪时的面部视频记录。随后，本文将用户参与度与注意力状态分析视为分类问题，探索单任务、迁移学习任务及多任务三种设置：单任务与迁移学习设置中，采用独立网络分别预测参与度与注意力状态；多任务设置中，则采用共享网络联合学习预测二者。此外，本文还考察了参与者在基于视频的问卷中的表现，并评估其感知认知负荷。研究发现：（a）在迁移学习与多任务学习中，参与度状态的分类性能均优于单任务学习；（b）更高的参与度与注意力状态与更低的认知负荷及更优的任务表现相关。该数据集与代码公开可获取，访问地址为https URL。
英文摘要
Active attention and engagement are important in improving users' learning experiences. Engagement refers to the level of involvement and interest individuals show towards a particular task. Attention, on the other hand, refers to a state where someone is entirely focused on a particular task with conscious awareness. Engagement and attention are different but closely linked concepts and can influence each other bidirectionally. To explore the relationship between user engagement and attention, we introduce the Diverse Reactions of Engagement and Attention Mind States (DREAMS) dataset. The dataset includes facial video recordings of 32 users in naturalistic settings watching various stimuli to evoke diverse emotions. We then analyze user engagement and attention states in these videos by framing it as a classification problem, exploring single-task, transfer learning task, and multi-task settings. In single and transfer learning task settings, separate networks are applied to predict engagement and attention states. Whereas in multi-task settings a shared network is applied, which jointly learns to predict both engagement and attention states. Moreover, we examine participants' performance on video-based questionnaires and evaluate their perceived cognitive workload. In our findings, we observe (a) better classification performance in predicting engagement states in both transfer and multi-task learning compared to single-task learning and (b) higher engagement and attention states correlate with lower cognitive load and improved task performance. The dataset and the code are publicly available and can be accessed through https://sites.google.com/view/dreams-dataset/dataset.
DOI：10.1007/978-3-031-78341-8_11
